Method and apparatus for training noise data determining model and determining noise data

Abstract

This application discloses training a noise data determining model and determining noise data. A method includes: obtaining sample noisy small molecule data and annotated noise data, the sample noisy small molecule data including data of a plurality of sample atoms; outputting a sample graph structure by using a neural network model based on the data of the plurality of sample atoms; performing prediction on the sample graph structure by using the neural network model, to obtain predicted noise data; and training the neural network model based on the predicted noise data and the annotated noise data, to obtain a noise data determining model. Final noise data in to-be-processed noisy small molecule data is determined by using the noise data determining model, so that denoising processing can be performed on the to-be-processed noisy small molecule data based on the final noise data, to obtain denoised small molecule dat

What is claimed is: 
     
         1 . A method for training a noise data determining model, performed by an electronic device, the method comprising:
 obtaining sample noisy small molecule data and annotated noise data, the sample noisy small molecule data being small molecule data with noise data and comprising data of a plurality of sample atoms, and the annotated noise data being noise data obtained from the sample noisy small molecule data through annotation;   outputting a sample graph structure by using a neural network model based on the data of the plurality of sample atoms, the sample graph structure comprising a plurality of sample nodes and a plurality of sample edges, each sample node representing data of a respective one of the plurality of sample atoms, and each sample edge representing a distance between respective sample atoms corresponding to two sample nodes at two ends of the sample edge;   performing prediction on the sample graph structure by using the neural network model, to obtain predicted noise data, the predicted noise data being noise data obtained from the sample noisy small molecule data through prediction; and   training the neural network model based on the predicted noise data and the annotated noise data, to obtain a noise data determining model, the noise data determining model being configured to determine final noise data in to-be-processed noisy small molecule data.   
     
     
         2 . The method according to  claim 1 , wherein the outputting a sample graph structure by using a neural network model based on the data of the plurality of sample atoms comprises:
 performing feature extraction on the data of the plurality of sample atoms respectively by using the neural network model, to obtain initial atomic features of the sample atoms;   obtaining data of a sample protein, and performing feature extraction on the data of the sample protein by using the neural network model, to obtain a feature of the sample protein; and   determining the sample graph structure by using the neural network model based on the initial atomic features of the sample atoms and the feature of the sample protein.   
     
     
         3 . The method according to  claim 2 , wherein the determining the sample graph structure by using the neural network model based on the initial atomic features of the sample atoms and the feature of the sample protein comprises:
 fusing, for each sample atom, the initial atomic feature of the each sample atom and the feature of the sample protein by using the neural network model, to obtain a first atomic feature of the each sample atom;   determining a first distance between each two sample atoms based on first atomic features of the sample atoms; and   determining the sample graph structure based on the first atomic features of the sample atoms and the first distance between each two sample atoms.   
     
     
         4 . The method according to  claim 1 , wherein the sample noisy small molecule data is initial noise data or is obtained by performing denoising processing on the initial noise data at least once; and
 the outputting a sample graph structure by using a neural network model based on the data of the plurality of sample atoms comprises:   obtaining sample number-of-times-of-denoising information, the sample number-of-times-of-denoising information representing a number of times of denoising processing performed to change the initial noise data to the sample noisy small molecule data; and   determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising information and the data of the plurality of sample atoms.   
     
     
         5 . The method according to  claim 4 , wherein the determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising information and the data of the plurality of sample atoms comprises:
 performing feature extraction on the sample number-of-times-of-denoising information by using the neural network model, to obtain a sample number-of-times-of-denoising feature;   performing feature extraction on the data of the plurality of sample atoms respectively by using the neural network model, to obtain initial atomic features of the sample atoms; and   determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising feature and the initial atomic features of the sample atoms.   
     
     
         6 . The method according to  claim 5 , wherein the determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising feature and the initial atomic features of the sample atoms comprises:
 fusing, for each sample atom, the initial atomic feature of the each sample atom and the sample number-of-times-of-denoising feature by using the neural network model, to obtain a second atomic feature of the each sample atom;   determining a second distance between each two sample atoms based on second atomic features of the sample atoms; and   determining the sample graph structure based on the second atomic features of the sample atoms and the second distance between each two sample atoms.   
     
     
         7 . The method according to  claim 5 , wherein the determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising feature and the initial atomic features of the sample atoms comprises:
 fusing, for each sample atom, the initial atomic feature of the each sample atom, the sample number-of-times-of-denoising feature, and a feature of a sample protein by using the neural network model, to obtain a third atomic feature of the each sample atom;   determining a third distance between each two sample atoms based on third atomic features of the sample atoms; and   determining the sample graph structure based on the third atomic features of the sample atoms and the third distance between each two sample atoms.   
     
     
         8 . The method according to  claim 1 , wherein the performing prediction on the sample graph structure by using the neural network model, to obtain predicted noise data comprises:
 performing feature extraction on the sample graph structure by using the neural network model, to obtain to-be-processed atomic features of the sample atoms;   determining, based on the to-be-processed atomic features of the sample atoms, at least one of predicted type noise data or predicted location noise data by using the neural network model, the predicted type noise data being noise data related to types of the sample atoms obtained through prediction, and the predicted location noise data being noise data related to locations of the sample atoms obtained through prediction; and   using the at least one of the predicted type noise data or the predicted location noise data as the predicted noise data.   
     
     
         9 . The method according to  claim 1 , wherein the performing prediction on the sample graph structure by using the neural network model, to obtain predicted noise data comprises:
 deleting, by using the neural network model, a first edge from the plurality of sample edges comprised in the sample graph structure, to obtain a first graph structure, a distance represented by the first edge being not greater than a reference distance;   determining first noise data by using the neural network model based on the first graph structure; and   determining the predicted noise data based on the first noise data.   
     
     
         10 . The method according to  claim 1 , wherein the performing prediction on the sample graph structure by using the neural network model, to obtain predicted noise data comprises:
 deleting, by using the neural network model, a second edge from the plurality of sample edges comprised in the sample graph structure, to obtain a second graph structure, a distance represented by the second edge being greater than a reference distance;   determining second noise data by using the neural network model based on the second graph structure; and   determining the predicted noise data based on the second noise data.   
     
     
         11 . The method according to  claim 1 , wherein the predicted noise data comprises predicted type noise data and predicted location noise data, and the annotated noise data comprises annotated type noise data and annotated location noise data; and
 the training the neural network model based on the predicted noise data and the annotated noise data, to obtain a noise data determining model comprises:   determining a first loss based on the predicted type noise data and the annotated type noise data;   determining a second loss based on the predicted location noise data and the annotated location noise data; and   training the neural network model based on the first loss and the second loss, to obtain the noise data determining model.   
     
     
         12 . A method for determining noise data, performed by an electronic device, the method comprising:
 obtaining to-be-processed noisy small molecule data, the to-be-processed noisy small molecule data being small molecule data with noise data and the to-be-processed noisy small molecule data comprising data of a plurality of to-be-processed atoms;   determining a to-be-processed graph structure by using a noise data determining model based on the data of the plurality of to-be-processed atoms, the to-be-processed graph structure comprising a plurality of nodes and a plurality of edges, each node representing a respective one to-be-processed atom, each edge representing a respective distance between to-be-processed atoms corresponding to two nodes at two ends of the edge; and   determining final noise data by using the noise data determining model based on the to-be-processed graph structure, the final noise data being noise data in the to-be-processed noisy small molecule data.   
     
     
         13 . The method according to  claim 12 , wherein the determining a to-be-processed graph structure by using a noise data determining model based on the data of the plurality of to-be-processed atoms comprises:
 performing feature extraction on the data of the plurality of to-be-processed atoms by using the noise data determining model, to obtain initial atomic features of the to-be-processed atoms;   obtaining data of a to-be-processed protein, and performing feature extraction on the data of the to-be-processed protein by using the noise data determining model, to obtain a feature of the to-be-processed protein; and   determining the to-be-processed graph structure by using the noise data determining model based on the initial atomic features of the to-be-processed atoms and the feature of the to-be-processed protein.   
     
     
         14 . The method according to  claim 12 , wherein the to-be-processed noisy small molecule data is initial noise data or is obtained by performing denoising processing on the initial noise data at least once; and
 the determining a to-be-processed graph structure by using a noise data determining model based on the data of the plurality of to-be-processed atoms comprises:   obtaining number-of-times-of-denoising information, the number-of-times-of-denoising information representing a number of times of denoising processing performed to change the initial noise data to the to-be-processed noisy small molecule data; and   determining the to-be-processed graph structure by using the noise data determining model based on the number-of-times-of-denoising information and the data of the plurality of to-be-processed atoms.   
     
     
         15 . The method according to  claim 12 , wherein the method further comprises:
 performing denoising processing on the to-be-processed noisy small molecule data based on the final noise data, to obtain first small molecule data; and   using the first small molecule data as target small molecule data in response to the first small molecule data meeting a data condition.   
     
     
         16 . The method according to  claim 15 , wherein the method further comprises:
 determining, in response to the first small molecule data not meeting the data condition, a reference graph structure by using the noise data determining model based on the first small molecule data;   determining reference noise data by using the noise data determining model based on the reference graph structure;   performing denoising processing on the first small molecule data based on the reference noise data, to obtain second small molecule data; and   using the second small molecule data as the target small molecule data in response to the second small molecule data meeting the data condition.   
     
     
         17 . An electronic device comprising:
 a memory storing a plurality of instructions; and   a processor configured to execute the plurality of instructions, wherein upon execution of the plurality of instructions, the processor is configured to cause the electric device to:
 obtain sample noisy small molecule data and annotated noise data, the sample noisy small molecule data being small molecule data with noise data and comprising data of a plurality of sample atoms, and the annotated noise data being noise data obtained from the sample noisy small molecule data through annotation; 
 output a sample graph structure by using a neural network model based on the data of the plurality of sample atoms, the sample graph structure comprising a plurality of sample nodes and a plurality of sample edges, each sample node representing data of a respective one of the plurality of sample atoms, and each sample edge representing a distance between respective sample atoms corresponding to two sample nodes at two ends of the sample edge; 
   perform prediction on the sample graph structure by using the neural network model, to obtain predicted noise data, the predicted noise data being noise data obtained from the sample noisy small molecule data through prediction; and   train the neural network model based on the predicted noise data and the annotated noise data, to obtain a noise data determining model, the noise data determining model being configured to determine final noise data in to-be-processed noisy small molecule data.   
     
     
         18 . The electronic device according to  claim 17 , wherein in order to output the sample graph structure by using the neural network model based on the data of the plurality of sample atoms, the processor, upon execution of the plurality of instructions, is configured to:
 perform feature extraction on the data of the plurality of sample atoms respectively by using the neural network model, to obtain initial atomic features of the sample atoms;   obtain data of a sample protein, and perform feature extraction on the data of the sample protein by using the neural network model, to obtain a feature of the sample protein; and   determine the sample graph structure by using the neural network model based on the initial atomic features of the sample atoms and the feature of the sample protein.   
     
     
         19 . The electronic device according to  claim 17 , wherein the sample noisy small molecule data is initial noise data or is obtained by performing denoising processing on the initial noise data at least once; and
 in order to output the sample graph structure by using the neural network model based on the data of the plurality of sample atoms, the processor, upon execution of the plurality of instructions, is configured to:   obtain sample number-of-times-of-denoising information, the sample number-of-times-of-denoising information representing a number of times of denoising processing performed to change the initial noise data to the sample noisy small molecule data; and   determining the sample graph structure by using the neural network model based on the sample number-of-times-of-denoising information and the data of the plurality of sample atoms.   
     
     
         20 . The electronic device according to  claim 17 , wherein in order to perform prediction on the sample graph structure by using the neural network model, to obtain predicted noise data, the processor, upon execution of the plurality of instructions, is configured to:
 perform feature extraction on the sample graph structure by using the neural network model, to obtain to-be-processed atomic features of the sample atoms;   determine, based on the to-be-processed atomic features of the sample atoms, at least one of predicted type noise data or predicted location noise data by using the neural network model, the predicted type noise data being noise data related to types of the sample atoms obtained through prediction, and the predicted location noise data being noise data related to locations of the sample atoms obtained through prediction; and   use the at least one of the predicted type noise data or the predicted location noise data as the predicted noise data.